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1. Using_Remote_Sensing_Data_and_Machine_Learning_Algorithms_for_Granular_Estimation_of_Poverty Using_Remote_Sensing_Data_and_Machine_Learning_Algorithms_for_Granular_Estimation_of_Poverty Public

   Remote Sensing + ML for Poverty Estimation Gained hands-on training in applying satellite imagery, geospatial analysis, and machine learning to estimate poverty at granular levels.

   Jupyter Notebook

2. Automated-Audio-Processing-to-Enhance-Data-Quality-in-Large-Scale-Field-Surveys Automated-Audio-Processing-to-Enhance-Data-Quality-in-Large-Scale-Field-Surveys Public

   Automated audio analysis pipeline for large-scale survey datasets. Includes preprocessing, feature extraction, and parallelized computation in R to improve efficiency and data quality.

   R

3. PovertyAction/ipaclean PovertyAction/ipaclean Public

   Stata

4. Estimating-Tourist-Site-Popularity-in-Senegal-Using-Social-Media-Data Estimating-Tourist-Site-Popularity-in-Senegal-Using-Social-Media-Data Public template

   Python

5. PovertyAction/ipa-research-data-science-hub PovertyAction/ipa-research-data-science-hub Public

   IPA Research and Data Science Hub

   R 3 2

6. Analyse-sociodemographique-et-typologique-des-eleveurs-de-moutons-Ladoum-au-Senegal Analyse-sociodemographique-et-typologique-des-eleveurs-de-moutons-Ladoum-au-Senegal Public

   L’objectif de ce projet est d’analyser des données sur les éleveurs de "ladoum" de la région de Thiès. Spécifiquement on cherchera à faire une typologie de ces éleveurs et à les caractériser pour m…

   R